1. Field of the Invention
The invention relates to the field of metrology and more particularly relates to a method for identifying measuring points in an optical measuring system in which a plurality of active or passive targets is used for marking measuring points.
2. Description of Related Art
Optical measuring systems are used for determining the position and orientation of objects in a three dimensional space, as well as for determining two dimensional deformation or three dimensional shape. For this purpose, optical measuring devices, such as cameras, laser measuring devices (e.g. laser trackers) or combinations thereof, are aimed towards the object to be measured and the spatial position of selected points (measuring points) on the object surface are registered. The data gained from such registration is then subjected to suitable computation from which data regarding the spatial position of the selected points is determined. The spatial position data of the selected points may then be used to obtain information regarding the position and orientation of the object.
The selected points on the object are usually marked with targets. These targets may be active, i.e., light emitting targets (e.g. light emitting diodes), or passive, i.e., light reflecting targets (e.g. reflectors, white spots on black background, black spots on white background or light spots projected onto the object).
As in most cases, a plurality of points is needed to get enough data for enabling computation of the desired information. It is important that registrations of measuring points can be correlated unequivocally to real measuring points. This correlation can be realized by sequential measurement of selectively illuminated targets, by image matching carried out by an operator, or by giving to each measuring point a unique, machine readable identity. Such machine readable identities may be the position of a measuring point in a predetermined geometric pattern of measuring points or it may be associated to the target marking the measuring point.
According to the state of the art, target associated identification is realized by code patterns, such as black and white patterns arranged in the vicinity of the target, and is usually decoded by template matching (image matching). Such code patterns may be circular segment codes or circular dot codes. Such codes are binary, i.e., for each code feature position, presence of the code feature (e.g. segment or dot) means “one”, whereas absence of the code feature means “zero”.
Such identification systems have some disadvantages. As the codes are binary, the number of permutations is relatively small, thereby necessitating large numbers of code features (segments or dots) for identifying large numbers of targets. In order to prevent misidentification, the code features must be arranged such that they can be recorded separately. Thus, large numbers of such features need a large amount of space, which restricts target density. As the code features are either present or absent, only present targets can be used for measurement. Machine reading of such identification patterns necessitates recordation and analysis of a large number of features (e.g. code segments or dots). Furthermore, decoding by template matching needs considerable computing capacity.